写一个图像分类的卷积神经网络代码

以下是一个简单的图像分类的卷积神经网络代码，仅供参考：

import tensorflow as tf

# 定义卷积神经网络模型
def cnn_model_fn(features, labels, mode):
    # 输入层
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    
    # 卷积层1
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)
    
    # 池化层1
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    
    # 卷积层2
    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)
    
    # 池化层2
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    
    # 全连接层
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(
        inputs=dense, rate=.4, training=mode == tf.estimator.ModeKeys.TRAIN)
    
    # 输出层
    logits = tf.layers.dense(inputs=dropout, units=10)
    
    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    
    # 计算损失函数
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    
    # 配置训练操作
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001)
        train_op = optimizer.minimize(
            loss=loss,
            global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
    
    # 配置评估操作
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

# 加载 MNIST 数据集
mnist = tf.contrib.learn.datasets.load_dataset("mnist")

# 创建 Estimator
mnist_classifier = tf.estimator.Estimator(
    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

# 配置训练和评估操作
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": mnist.train.images},
    y=mnist.train.labels.astype(int),
    batch_size=100,
    num_epochs=None,
    shuffle=True)
mnist_classifier.train(
    input_fn=train_input_fn,
    steps=20000,
    hooks=None)

eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": mnist.test.images},
    y=mnist.test.labels.astype(int),
    num_epochs=1,
    shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)

这个代码实现了一个简单的卷积神经网络模型，用于对 MNIST 数据集中的手写数字进行分类。具体实现过程可以参考代码中的注释。